This is how a chronicle report looks
This is a showcase of the outputs possible with the {chronicle} R package. For a complete how-to, please refer to the package’s github page.
Barplots
chronicle::add_barplot()
Simple bar plot
Bars broken by other group
Bars sorted by value
Bars sorted by value and broken by another column
Horizontally
Boxplots
chronicle::add_boxplot()
Simple box plot
Box plot by groups
Box plot with jitter
Code
chronicle::add_code()
This is an empty canvas for you to include any code you want.
This is some code:
data.table(iris)[, .N, Species]It can also be evaluated!
library(data.table)
data.table(iris)[, .N, Species]## Species N
## 1: setosa 50
## 2: versicolor 50
## 3: virginica 50Densities
chronicle::add_density()
Basic density
Density by group
Faceted densities
dygraphs
chronicle::add_dygraph()
Simple dygraph (in static outputs it will be replaced by line plots)
Dygraph by groups
Histograms()
chronicle::add_histogram
Basic histogram
Faceted histogram by groups
Line plots
chronicle::add_lineplot()
Simple line plot
Line plot with trend
Line plot with linear trend
Faceted line plot
Rain cloud plots
chronicle::add_raincloud()
Simple rain cloud
Rain cloud by group
Larger denisty kernel
No boxplot, just the median
With the mean instead of the boxplot
Scatter plots
chronicle::add_scatterplot()
Simple scatter plot
Scatter plot with groups
Faceted scatter plot with trend
Tables
chronicle::add_table()
kable
| Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species |
|---|---|---|---|---|
| 5.1 | 3.5 | 1.4 | 0.2 | setosa |
| 4.9 | 3.0 | 1.4 | 0.2 | setosa |
| 4.7 | 3.2 | 1.3 | 0.2 | setosa |
| 4.6 | 3.1 | 1.5 | 0.2 | setosa |
| 5.0 | 3.6 | 1.4 | 0.2 | setosa |
| 5.4 | 3.9 | 1.7 | 0.4 | setosa |
DT
Violins
chronicle::add_violin()
Simple violin plot
Violin plot by group
Big Bonus! report_columns()
chronicle also includes a function called report_columns(), that will create an entire chronicle report for a single dataset. It includes a comprehensive summary of the data through the skimr::skim() function, along with one plot for each column present in the data: bar plots for categorical variables and rain cloud plots for numerical variables. This gives you an immediate view of a dataset with a single line of code!
report_columns(dt = palmerpenguins::penguins,
by_column = 'species')you can see the example of this output here
sessionInfo
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19042)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=English_United States.1252 LC_CTYPE=English_United States.1252 LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C LC_TIME=English_United States.1252
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] stringr_1.4.0 chronicle_0.2.5 data.table_1.13.6 magrittr_2.0.1 rlang_0.4.10
##
## loaded via a namespace (and not attached):
## [1] tufte_0.9 lattice_0.20-41 tidyr_1.1.2 ps_1.5.0 zoo_1.8-8 digest_0.6.27
## [7] R6_2.5.0 evaluate_0.14 httr_1.4.2 xaringan_0.19 ggplot2_3.3.3 highr_0.8
## [13] pillar_1.4.7 tictoc_1.0 lazyeval_0.2.2 rstudioapi_0.13 callr_3.5.1 Matrix_1.2-18
## [19] DT_0.17 rmarkdown_2.6 labeling_0.4.2 splines_4.0.3 webshot_0.5.2 flexdashboard_0.5.2
## [25] rolldown_0.1 readr_1.4.0 htmlwidgets_1.5.3 munsell_0.5.0 tinytex_0.28 compiler_4.0.3
## [31] xfun_0.20 pkgconfig_2.0.3 mgcv_1.8-33 htmltools_0.5.0 tidyselect_1.1.0 tibble_3.0.4
## [37] gridExtra_2.3 bookdown_0.21 viridisLite_0.3.0 crayon_1.3.4 dplyr_1.0.2 withr_2.3.0
## [43] grid_4.0.3 nlme_3.1-151 jsonlite_1.7.2 gtable_0.3.0 lifecycle_0.2.0 scales_1.1.1
## [49] rmdformats_1.0.1 stringi_1.5.3 farver_2.0.3 viridis_0.5.1 dygraphs_1.1.1.6 ellipsis_0.3.1
## [55] xts_0.12.1 generics_0.1.0 vctrs_0.3.6 tools_4.0.3 glue_1.4.2 purrr_0.3.4
## [61] hms_0.5.3 crosstalk_1.1.0.1 prettydoc_0.4.1 processx_3.4.5 yaml_2.2.1 colorspace_2.0-0
## [67] plotly_4.9.3 knitr_1.31 pagedown_0.13